To this date, there have been examples of fusion of one image data set onto another image data set so as to register anatomical images seen in both data sets. One example of this is the work of Pellizari and Chen. They were able to take, for example, an MRI image data set of the brain and a similar CT image data set and surface render the surface of the brain in each of the data sets. They then used a computer algorithm to "stretch" one data set of the brain surface onto the other data set of the brain surface to produce the best registration. This is done using a reduction of the surface to discrete points and minimizing a distance function of the two data sets so as to best fit one to the other. Although the two data sets may be taken from two-dimensional tomographic slices and then stacked into a three-dimensional data set volume, the surfaces of the anatomy or other structures may be segmented or separated out into their own sub-set for the purposes of such image fusion.
Another example of prior art is the work of one of the authors, Marcelle Herk, in registration of two-dimensional images seen from a portal imager on a linear accelerator (LINAC), and comparing that to other two-dimensional images taken from previous X-rays. The technique which was used is the so-called "Chamfer technique," which relates to a distance transform and a minimization principle to map two similar anatomical structures or features onto each other. Separation of a sub-set of anatomical data can be done by a process called segmentation, which separates out the sub-structures and related data points based on, for example, intensity or other image parameters. This would enable, for example, the skull, the ventricles, or the cortex of the brain to be segmented in an MRI or a CT image.
There is, however, an outstanding problem in medical imaging which heretofore was not resolved. That is to take a data set such as MRI imaging, which is rendered in a three-dimensional volume based on a series of two-dimensional slices or a three-dimensional data collection set, and relate it to a stereotactically derived CT image data set of the same anatomy. The problem with the MRI data set is that it typically has intrinsic distortions. The problem with a CT data set is that although each individual two-dimensional slice of a stack of two-dimensional images may have a good metric or distance dimension from the CT scan, it is often unknown where the slice is in relation to external apparatus or body-fixed apparatus. By doing a proper transformation of the CT data set into a stereotactic space related to an external apparatus, these problems can be overcome.
It is thus an object of the present invention to provide a means whereby, in combination, two large image data sets, such as for an MRI image data set and a CT data set, can be fused together by a computer algorithm, and one of the data sets can be put into a faithful stereotactic frame of reference so that all of the points in three dimensions have an accurate spatial representation relative to each other. Thereafter, the second data set, which may have intrinsic distortion, can be fused with the first data set, which is rendered in a known stereotactic coordinate system, thereby providing an accurate dimension scale and stereotactic coordinate set for the second distorted data set. An example of this is to take a distorted MRI image data set with no stereotactic reference markers and fuse it with a CT data set, which has been acquired, with a stereotactic localizer in place on the patient's body, thereby providing an accurate rendering of the MRI data in stereotactic coordinates.